A new focus evaluation operator based on max–min filter and its application in high quality multi-focus image fusion

Liu, Shuaiqi; Lu, Yucong; Wang, Jie; Hu, Shaohai; Zhao, Jie; Zhu, Zhongkui

doi:10.1007/s11045-019-00675-2

Cited by 20 publications

(10 citation statements)

References 20 publications

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“…Then, we use two separate loss functions to normalize the upper and lower channels. The loss functions of Net-S and Net-D are shown in formulas (11) and (12).…”

Section: A Image Super-resolutionmentioning

confidence: 99%

“…where S gt and D gt are the corresponding ground-truth values of Net-S and Net-D output. Combining the equations (10)- (12), the overall loss function of the DualCNN can be defined as:…”

Section: A Image Super-resolutionmentioning

confidence: 99%

“…Among the representative algorithms is image fusion based on Laplace pyramid transform [7], discrete wavelet transform [8], curvelet transform [9], contourlet transform [10], and shearlet transform [11] and so on. Spatial domain-based methods include image fusion based on: max-min filtering [12], image block matching [13], guided filter [14], dense-scale feature-invariant methods [15] and multi-focus image fusion algorithms based on boundary discovery [16]. Methods based on sparse representation include multi-focus image fusion based on sparse representation [17], convolution sparse representation [18], dictionary sparse representation [19], multi-scale transform and sparse representation [20], and so on.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Focus Color Image Fusion Algorithm Based on Super-Resolution Reconstruction and Focused Area Detection

Liu

Yin

et al. 2020

IEEE Access

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Multi-focus image fusion is an image processing that generates an integrated image by merging multiple images from different focus area in the same scene. For most fusion methods, the detection of the focus area is a critical step. In this paper, we propose a multi-focus image fusion algorithm based on a dual convolutional neural network (DualCNN), in which the focus area is detected from super-resolved images. Firstly, the source image is input into a DualCNN to restore the details and structure from its superresolved image, as well as to improve the contrast of the source image. Secondly, the bilateral filter is used to reduce noise on the fused image, and the guided filter is used to detect the focus area of the image and refine the decision map. Finally, the fused image is obtained by weighting the source image according to the decision map. Experimental results show that our algorithm can well retain image details and maintain spatial consistency. Compared with existing methods in multiple groups of experiments, our algorithm can achieve better visual perception according to subjective evaluation and objective indexes. INDEX TERMS Multi-focus image fusion, super-resolution, dual convolutional neural network, focus area detection.

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“…Then, we use two separate loss functions to normalize the upper and lower channels. The loss functions of Net-S and Net-D are shown in formulas (11) and (12).…”

Section: A Image Super-resolutionmentioning

confidence: 99%

“…where S gt and D gt are the corresponding ground-truth values of Net-S and Net-D output. Combining the equations (10)- (12), the overall loss function of the DualCNN can be defined as:…”

Section: A Image Super-resolutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Focus Color Image Fusion Algorithm Based on Super-Resolution Reconstruction and Focused Area Detection

Liu

Yin

et al. 2020

IEEE Access

Self Cite

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“…However, one model cannot capture enough information due to limitations of the system because it is hard to capture focus for all objects due to the limited focal length of camera. Therefore, combining images of different focal lengths for the same scenery by varying the focal length is known as multi-focus image fusion [8][9][10][11]. Similarly, CT and MRI images are used to diagnose many medical conditions This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Image Fusion Algorithm by Combined Histogram Equalization and Fast Gray Level Grouping Using Multi-Scale Decomposition and Gray-PCA

Bhutto

et al. 2020

IEEE Access

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Image enhancement is a challenging task in image analysis particularly, it is more challenging in performing image fusion. Image fusion is the process of combining multiple images to produce quality output without any variation in contrast, blurring, and noise. Many image fusion algorithms have been implemented, but their final fused images suffer from variations in background contrast, uneven illumination, blurring, and the presence of noise. To overcome the aforementioned issues, this paper proposed a new image fusion method, which improves image contrast and also gives appropriate details of the image. Our method is based on a set of conventional techniques such as amalgamated histogram equalization and fast gray-scale grouping to handle the problems mentioned, and we improve overall fusion strategies by proposing a novel principal component analysis technique to convert RGB types images to high gray-scale contrast image as the final output image. We have carried out many experiments on different common databases used by various researchers. Our proposed method gives good subjective and objective performances compared to other statuses. Our proposed method can be used in different monitoring applications. INDEX TERMS Histogram equalization and fast gray-level grouping (HEFGLG), Image Fusion, Multifocus image, Infrared image, Visible image, Average pixel intensity, Cross Correlation.

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“…Furthermore, the author used a bilateral filter to reduce noise and retain image spatial consistency. In [35] a new max -min filter-based focus assessment operator is proposed. Combining average filter and median filter (MMAM) a maxmin filter is used to determine the degree of concentration of the source images.…”

Section: Introductionmentioning

confidence: 99%

MSEF-ImgSeg: An Intelligent Algorithm for Multi Scale Exposure Fusion Using Image Segmentation and GGIF

et al. 2020

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Multi-scale exposure fusion is a powerful approach to fuse variously low dynamic range images into a high quality image. Fine and attractive information of source images is added in the resultant image. It may yield better fusion result than the images fused by single scale exposure fusion. In multi-scale exposure fusion possibility of halo artifact can be produced and the details in darkest and brightest areas are normally not retained in the final fused image. Based on image segmentation and edge preserving filtration technique a novel algorithm is proposed in this paper for multi-scale exposure images. Taking advantage of super pixel (image segmentation) along with edge preserving technique, details in the darkest and brightest regions are well protected which removes the halo artifacts produced in the fused image. The outcome of experiments proved that proposed algorithm provides better results than other state of the art algorithms used for image fusion.

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A new focus evaluation operator based on max–min filter and its application in high quality multi-focus image fusion

Cited by 20 publications

References 20 publications

Multi-Focus Color Image Fusion Algorithm Based on Super-Resolution Reconstruction and Focused Area Detection

Multi-Focus Color Image Fusion Algorithm Based on Super-Resolution Reconstruction and Focused Area Detection

An Enhanced Image Fusion Algorithm by Combined Histogram Equalization and Fast Gray Level Grouping Using Multi-Scale Decomposition and Gray-PCA

MSEF-ImgSeg: An Intelligent Algorithm for Multi Scale Exposure Fusion Using Image Segmentation and GGIF

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